{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:SF5RRLVCAS3OMTEPWE2VKII4SV","short_pith_number":"pith:SF5RRLVC","canonical_record":{"source":{"id":"2412.16227","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-18T18:40:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1ce1616b78c473d5e589b2c43ac2503af8685ed13748cc85b3b55e1142b5d1a5","abstract_canon_sha256":"7822f650ddca356a55178219791845a0d5b14606de2cca6162835c02bddd0b8a"},"schema_version":"1.0"},"canonical_sha256":"917b18aea204b6e64c8fb13555211c95436489e6601a66936d9f44be77b16e03","source":{"kind":"arxiv","id":"2412.16227","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.16227","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"arxiv_version","alias_value":"2412.16227v1","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16227","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"pith_short_12","alias_value":"SF5RRLVCAS3O","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"pith_short_16","alias_value":"SF5RRLVCAS3OMTEP","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"pith_short_8","alias_value":"SF5RRLVC","created_at":"2026-07-05T09:52:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:SF5RRLVCAS3OMTEPWE2VKII4SV","target":"record","payload":{"canonical_record":{"source":{"id":"2412.16227","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-18T18:40:21Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"1ce1616b78c473d5e589b2c43ac2503af8685ed13748cc85b3b55e1142b5d1a5","abstract_canon_sha256":"7822f650ddca356a55178219791845a0d5b14606de2cca6162835c02bddd0b8a"},"schema_version":"1.0"},"canonical_sha256":"917b18aea204b6e64c8fb13555211c95436489e6601a66936d9f44be77b16e03","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:43.357064Z","signature_b64":"I6VDlN+ANoEbkAcpl/py7K9rG9PhLjm1blCgiyW0TJO3ncs03uQHroTbIDKJEOjssLpI/p1ewC2f3Zzo1sUyDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"917b18aea204b6e64c8fb13555211c95436489e6601a66936d9f44be77b16e03","last_reissued_at":"2026-07-05T09:52:43.356552Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:43.356552Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2412.16227","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:52:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J5JEuWfBf4At89hIfSK9ZExOw4xCO/DKveJ1wgFJKASYOEmgE3hbnkZitpcb1R7mfrxnJyq9fQxpbM0pcYDtAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T05:59:08.374613Z"},"content_sha256":"22c89ca2f65b81164ca0f441700be45350f0a5d255c098e6b8c60d1b39ca443a","schema_version":"1.0","event_id":"sha256:22c89ca2f65b81164ca0f441700be45350f0a5d255c098e6b8c60d1b39ca443a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:SF5RRLVCAS3OMTEPWE2VKII4SV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hanbin Hong, Shenao Yan, Shuya Feng, Yan Yan, Yuan Hong","submitted_at":"2024-12-18T18:40:21Z","abstract_excerpt":"Active Learning (AL) represents a crucial methodology within machine learning, emphasizing the identification and utilization of the most informative samples for efficient model training. However, a significant challenge of AL is its dependence on the limited labeled data samples and data distribution, resulting in limited performance. To address this limitation, this paper integrates the zero-shot text-to-image (T2I) synthesis and active learning by designing a novel framework that can efficiently train a machine learning (ML) model sorely using the text description. Specifically, we leverage"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16227","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2412.16227/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:52:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kTMXQaRCaGAGGhraB7UEdJcx02XSjQtBkbaod8TIbSNh/vU7uJz7SwzDQScTd9WNca5dlYK0YWNjLzrBjeG9AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T05:59:08.374992Z"},"content_sha256":"9a6ec20767994210970563bb95f7716e548aefc12d8fc6a60b61a6ef48e57cc7","schema_version":"1.0","event_id":"sha256:9a6ec20767994210970563bb95f7716e548aefc12d8fc6a60b61a6ef48e57cc7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SF5RRLVCAS3OMTEPWE2VKII4SV/bundle.json","state_url":"https://pith.science/pith/SF5RRLVCAS3OMTEPWE2VKII4SV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SF5RRLVCAS3OMTEPWE2VKII4SV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-10T05:59:08Z","links":{"resolver":"https://pith.science/pith/SF5RRLVCAS3OMTEPWE2VKII4SV","bundle":"https://pith.science/pith/SF5RRLVCAS3OMTEPWE2VKII4SV/bundle.json","state":"https://pith.science/pith/SF5RRLVCAS3OMTEPWE2VKII4SV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SF5RRLVCAS3OMTEPWE2VKII4SV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:SF5RRLVCAS3OMTEPWE2VKII4SV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7822f650ddca356a55178219791845a0d5b14606de2cca6162835c02bddd0b8a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-18T18:40:21Z","title_canon_sha256":"1ce1616b78c473d5e589b2c43ac2503af8685ed13748cc85b3b55e1142b5d1a5"},"schema_version":"1.0","source":{"id":"2412.16227","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.16227","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"arxiv_version","alias_value":"2412.16227v1","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.16227","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"pith_short_12","alias_value":"SF5RRLVCAS3O","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"pith_short_16","alias_value":"SF5RRLVCAS3OMTEP","created_at":"2026-07-05T09:52:43Z"},{"alias_kind":"pith_short_8","alias_value":"SF5RRLVC","created_at":"2026-07-05T09:52:43Z"}],"graph_snapshots":[{"event_id":"sha256:9a6ec20767994210970563bb95f7716e548aefc12d8fc6a60b61a6ef48e57cc7","target":"graph","created_at":"2026-07-05T09:52:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2412.16227/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Active Learning (AL) represents a crucial methodology within machine learning, emphasizing the identification and utilization of the most informative samples for efficient model training. However, a significant challenge of AL is its dependence on the limited labeled data samples and data distribution, resulting in limited performance. To address this limitation, this paper integrates the zero-shot text-to-image (T2I) synthesis and active learning by designing a novel framework that can efficiently train a machine learning (ML) model sorely using the text description. Specifically, we leverage","authors_text":"Hanbin Hong, Shenao Yan, Shuya Feng, Yan Yan, Yuan Hong","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-18T18:40:21Z","title":"GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.16227","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:22c89ca2f65b81164ca0f441700be45350f0a5d255c098e6b8c60d1b39ca443a","target":"record","created_at":"2026-07-05T09:52:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7822f650ddca356a55178219791845a0d5b14606de2cca6162835c02bddd0b8a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-18T18:40:21Z","title_canon_sha256":"1ce1616b78c473d5e589b2c43ac2503af8685ed13748cc85b3b55e1142b5d1a5"},"schema_version":"1.0","source":{"id":"2412.16227","kind":"arxiv","version":1}},"canonical_sha256":"917b18aea204b6e64c8fb13555211c95436489e6601a66936d9f44be77b16e03","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"917b18aea204b6e64c8fb13555211c95436489e6601a66936d9f44be77b16e03","first_computed_at":"2026-07-05T09:52:43.356552Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:52:43.356552Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"I6VDlN+ANoEbkAcpl/py7K9rG9PhLjm1blCgiyW0TJO3ncs03uQHroTbIDKJEOjssLpI/p1ewC2f3Zzo1sUyDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:52:43.357064Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.16227","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:22c89ca2f65b81164ca0f441700be45350f0a5d255c098e6b8c60d1b39ca443a","sha256:9a6ec20767994210970563bb95f7716e548aefc12d8fc6a60b61a6ef48e57cc7"],"state_sha256":"78b2050a391c83d94697ded8f86677db026e1c915d9e29795945b180a0505337"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WAtCRLQ/qluQM3QgEaKCmAlsI8WV56sHPC763Zutn0q+U6yI6m/1EPbnHzxCt7+Q9o0T8lHGrzqY6Sjf74MHDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T05:59:08.376931Z","bundle_sha256":"91eedd95d12258921c3c78fa4724ac4061011c3182f3766e7ffdae62b109a611"}}